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学者姓名：郭谋发
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High impedance faults (HIFs) are difficult to detect because of low amplitude of the current signal. The interference from switching cases also jeopardizes the reliability of HIF detection (HIFD). Moreover, a long detection time is more likely to cause accidents. To diagnose HIFs promptly and precisely, a time-adaptive (TA) HIFD model is proposed. Firstly, the zero-sequence current data of the faulty feeder are processed into a variable length training set to train gated recurrent unit (GRU) networks. Secondly, a cost-sensitive method employed to train two biased GRU models with contrary preference. Then the two models are combined into high reliability evaluation model. The predicted reliability depends on the consistency of predicted results of the two models. Reliable results are output, while the unreliable results are set aside. To prevent untimely detection, an equal GRU-based model is activated when reaching the time threshold. Delayed judgment improves accuracy of HIFD and reduces probability of harm caused by untimely detection. The performance of proposed method validated by simulated data, and tested in a realistic 10 kV distribution network experimental system. In the true type 10kV system, the TA HIFD model can achieve an accuracy of 96.73% with average detection time of 4.315ms.
Keyword ：
cost-sensitive cost-sensitive fault detection fault detection gated recurrent unit (GRU) gated recurrent unit (GRU) high impedance fault (HIF) high impedance fault (HIF) time-adaptive (TA) time-adaptive (TA) variable mode decomposition (VMD) variable mode decomposition (VMD)
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| GB/T 7714 | Lin, Jianxin , Lin, Xiwen , Wang, Huaiyuan et al. Time-adaptive High Impedance Fault Detection Model Based on Cost-sensitive Method [J]. | ELECTRIC POWER SYSTEMS RESEARCH , 2025 , 247 . |
| MLA | Lin, Jianxin et al. "Time-adaptive High Impedance Fault Detection Model Based on Cost-sensitive Method" . | ELECTRIC POWER SYSTEMS RESEARCH 247 (2025) . |
| APA | Lin, Jianxin , Lin, Xiwen , Wang, Huaiyuan , Guo, Moufa . Time-adaptive High Impedance Fault Detection Model Based on Cost-sensitive Method . | ELECTRIC POWER SYSTEMS RESEARCH , 2025 , 247 . |
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Existing single-phase ground fault mitigation device (SPGFMD) is usually equipped with a grounding terminal, but the resistance of the grounding grid may change during the long-term operation of the power system. To analyze the influence of the grounding grid resistance on the effectiveness of ground fault current mitigation, the relationship between the grounding grid resistance and the residual ground fault current is derived. This article conducts simulation and experiment to investigate the impact of various grounding grid resistances on the suppression effect of ground fault current under different ground transition resistance circumstances. The critical grounding grid resistance that affects the single-phase ground fault current mitigation effect has been identified, offering a theoretical foundation for the on-site evaluation of the grounding grid status of SPGFMD. The influence mechanism of grounding grid resistance is verified by the simulation and experiment results. The results indicate that the SPGFMD with current-based method can withstand a certain range of grounding grid resistance variation under the condition of a single-phase ground fault in the distribution networks. However, the effectiveness of ground fault current mitigation deteriorates when the grounding grid resistance exceeds the critical threshold. © 2025 John Wiley & Sons Ltd.
Keyword ：
current-based method current-based method distribution networks distribution networks grounding grid resistance grounding grid resistance single-phase ground fault current mitigation single-phase ground fault current mitigation
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| GB/T 7714 | Guo, M. , Weng, M. , Zheng, Z. et al. Single-Phase Ground Fault Current Mitigation in Distribution Network Considering the Influence of Grounding Grid Resistance [J]. | International Journal of Circuit Theory and Applications , 2025 . |
| MLA | Guo, M. et al. "Single-Phase Ground Fault Current Mitigation in Distribution Network Considering the Influence of Grounding Grid Resistance" . | International Journal of Circuit Theory and Applications (2025) . |
| APA | Guo, M. , Weng, M. , Zheng, Z. , Lin, S. . Single-Phase Ground Fault Current Mitigation in Distribution Network Considering the Influence of Grounding Grid Resistance . | International Journal of Circuit Theory and Applications , 2025 . |
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Identifying fault sections in single-phase ground (SPG) faults is essential for electric utilities to promptly isolate faults and restore service. A deep learning-based approach leveraging feature fusion has been proposed for SPG fault section location, utilizing transient zero-sequence currents (TZSCs) captured by feeder terminal units (FTUs). Initially, a convolutional neural network (ConvNet) is pre-trained on TZSC waveforms to distinguish data from the upstream and downstream of the fault point, acting as a feature extractor. This pre-training enables the model to capture distinct transient characteristics from both ends of the fault. The pre-trained ConvNet is then replicated to form a dual-branch architecture, where TZSC data from both ends of the feeder section are input into the respective branches. The features extracted from these branches are concatenated at a fusion layer, allowing the model to effectively integrate the transient information from upstream and downstream, leading to more precise fault section location. Compared with existing methods, our approach demonstrates robustness under various conditions, including simulation verification and field verification. Extensive testing shows that the model maintains high performance even with limited field data, and fine-tuning further enhances its practical applicability for engineering. Moreover, an industrial prototype utilizing Raspberry Pi 4B has been implemented in real-world distribution networks, where fault data are transmitted to the main station, further optimizing the fault section location process using our proposed approach.
Keyword ：
Fault section location Fault section location Feature fusion Feature fusion One-dimension convolutional neural network One-dimension convolutional neural network Resonant distribution networks Resonant distribution networks
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| GB/T 7714 | Gao, Jian-Hong , Guo, Mou-Fa , Lin, Shuyue et al. Deep learning approach for single-phase ground fault section location via feature fusion in resonant distribution networks [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 268 . |
| MLA | Gao, Jian-Hong et al. "Deep learning approach for single-phase ground fault section location via feature fusion in resonant distribution networks" . | EXPERT SYSTEMS WITH APPLICATIONS 268 (2025) . |
| APA | Gao, Jian-Hong , Guo, Mou-Fa , Lin, Shuyue , Chen, Duan-Yu , Bai, Hao . Deep learning approach for single-phase ground fault section location via feature fusion in resonant distribution networks . | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 268 . |
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In order to address the challenges posed by weak and variable high-impedance fault signals and limited data availability in practical distribution networks, a novel method for detecting high-impedance faults is proposed. Initially, a multi-head variational autoencoder model based on squeeze-excitation networks is employed to augment the small sample dataset. Subsequently, the data are filtered, and the temporal and frequency domain features are extracted, respectively. Considering the weak characteristics of high impedance fault features and the limitations of the proliferation model in generating comprehensive and effective fault features, a categorical boosting algorithm based on the gradient harmonized mechanism (GHM-CatBoost) is introduced. The GHM-CatBoost algorithm incorporates a gradient harmonized mechanism loss function to address the imbalance in attention between easily distinguishable and challenging samples, thereby mitigating the issue of overfitting. The research findings suggest that the data proliferation model can produce fault samples with a blend of simulation data diversity and measured data randomness, thereby enhancing the richness of the dataset. Furthermore, the fault recognition accuracy achieved by the proposed GHM-CatBoost model is notably high at 97.21%, outperforming its counterpart classifier model. Moreover, the efficacy of the proposed approach is validated through rigorous testing and comparative analysis. © 2025 Science Press. All rights reserved.
Keyword ：
Adaptive boosting Adaptive boosting Fault detection Fault detection Frequency domain analysis Frequency domain analysis Image segmentation Image segmentation Network coding Network coding Variational techniques Variational techniques
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| GB/T 7714 | Gao, Wei , He, Wenxiu , Guo, Moufa et al. Detection Method of High-impedance Fault in Distribution Network Based on Uneven Small Samples from Actual Measurements [J]. | High Voltage Engineering , 2025 , 51 (3) : 1135-1144 . |
| MLA | Gao, Wei et al. "Detection Method of High-impedance Fault in Distribution Network Based on Uneven Small Samples from Actual Measurements" . | High Voltage Engineering 51 . 3 (2025) : 1135-1144 . |
| APA | Gao, Wei , He, Wenxiu , Guo, Moufa , Bai, Hao . Detection Method of High-impedance Fault in Distribution Network Based on Uneven Small Samples from Actual Measurements . | High Voltage Engineering , 2025 , 51 (3) , 1135-1144 . |
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Complex single-phase ground fault (SPGF) is a challenging problem for early detection and type recognition in resonant distribution networks. This paper proposes a novel semantic-segmentation-based approach that leverages the morphological information of zero-sequence voltage signals to extract diverse semantic features representing fault inception (FI), fault disappearance (FD), and short-term transient fault (STF). A 1D-UNet model is employed to classify each sample point into one of these categories, which enables the determination of the moment and duration of SPGF. Based on these features, three types of SPGF are recognized: permanent fault (PF), long-term transient fault(LTF), and short-term transient fault (STF). Due to its low power consumption and costeffectiveness, an industrial prototype integrated with the proposed approach has been developed using a Raspberry Pi board. The proposed approach achieves an overall accuracy of over 94 % in classifying sample points across diverse categories. Specifically, the individual accuracies for detecting sample points belonging to FI, FD, and STF were 0.978, 0.968, and 0.971, respectively. From an engineering application perspective, the proposed approach effectively identifies the moment of fault occurrence, whether it is PF, LTF, or STF. The maximum, minimum, and median triggering deviations were 10.8 ms,-6.4 ms, and-0.4 ms, respectively, significantly outperforming existing methods in terms of fault moment triggering deviation. The experimental results demonstrate that the proposed approach works effectively for early detection and type recognition of SPGF, showcasing significant potential for further expansion and broader application.
Keyword ：
Early detection Early detection Resonant distribution networks Resonant distribution networks Semantic segmentation Semantic segmentation Single-phase ground fault Single-phase ground fault Type recognition Type recognition
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| GB/T 7714 | Gao, Jian-Hong , Guo, Mou-Fa , Lin, Shuyue et al. Semantic-segmentation-based approach for early detection and type recognition of single-phase ground fault in resonant distribution networks [J]. | APPLIED SOFT COMPUTING , 2025 , 171 . |
| MLA | Gao, Jian-Hong et al. "Semantic-segmentation-based approach for early detection and type recognition of single-phase ground fault in resonant distribution networks" . | APPLIED SOFT COMPUTING 171 (2025) . |
| APA | Gao, Jian-Hong , Guo, Mou-Fa , Lin, Shuyue , Chen, Duan-Yu , Bai, Hao . Semantic-segmentation-based approach for early detection and type recognition of single-phase ground fault in resonant distribution networks . | APPLIED SOFT COMPUTING , 2025 , 171 . |
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The existing single-phase grounding (SPG) fault section location methods typically suffer from difficulty in feature selection, limited feeder terminal units (FTUs) configuration, and excessive dependence on communication, which weaken their generalization and robustness. To overcome these challenges, an SPG fault section location approach based on feature subset optimization is proposed. First, the relation between the position of FTU and its three-phase current variation is analyzed, and its fault features are extracted to construct the candidate feature sets as feature subset optimization objects. Then, genetic algorithm and support vector machine (SVM) are combined to select the optimal feature subset with small dimensions and recognition error, which avoids the empirical errors of artificial feature selection. To reduce the cumulative errors, the SVM hyperparameters are simultaneously optimized. Finally, the SVM model is trained based on the optimal feature subset and hyperparameters. In the absence of zero-sequence current measurement, three-phase currents measured by FTU are locally processed to locate the fault section by the trained SVM. The experimental results verified the effectiveness and feasibility of the proposed method. In this paper, a single-phase grounding fault section location method is proposed by using genetic algorithm and support vector machine (SVM) to achieve feature subset optimization. In the process of section identification, the empirical error caused by artificially selecting features is avoided, and cumulative errors of multiple links such as feature selection, parameter optimization, and model training are reduced. Additionally, the communication and measurement requirements are reduced since feeder terminal unit only needs to measure local three-phase current information. image
Keyword ：
distribution networks distribution networks fault section location fault section location feature subset optimization feature subset optimization genetic algorithm (GA) genetic algorithm (GA) single-phase grounding fault single-phase grounding fault support vector machine (SVM) support vector machine (SVM)
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| GB/T 7714 | Bai, Hao , Chen, Mu-Yan , Guo, Mou-Fa et al. Fault section location in resonant grounding distribution systems based on feature subset optimization of phase current variation [J]. | INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS , 2024 , 52 (9) : 4582-4599 . |
| MLA | Bai, Hao et al. "Fault section location in resonant grounding distribution systems based on feature subset optimization of phase current variation" . | INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS 52 . 9 (2024) : 4582-4599 . |
| APA | Bai, Hao , Chen, Mu-Yan , Guo, Mou-Fa , Liu, Yi-Peng , Gao, Jian-Hong . Fault section location in resonant grounding distribution systems based on feature subset optimization of phase current variation . | INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS , 2024 , 52 (9) , 4582-4599 . |
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The existing residual current device (RCD) operates based on the amplitude of the residual current, but if the threshold is not reasonably set, the RCD is prone to reject or misoperate. Therefore, identifying biological electric-shock faults from grounding faults is a crucial approach. Current research only selects one or several features without following proper feature selection rules. Furthermore, machine learning methods require a certain number of samples to train the model to ensure algorithm accuracy and stability. However, obtaining a large number of biological electric-shock samples is challenging during actual experiments, and the algorithm model cannot learn the waveform in real settings. To solve the above problems, a biological electric-shock fault identification method based on multi-feature optimization selection under unbalanced small samples is proposed. Firstly, variational auto-encoders (VAE) is adopted to multiply the electric-shock small sample data collected by experiments to achieve positive and negative sample balance. Due to the complexity and danger of the scenes, it is difficult to obtain the actual electric-shock samples. The problem of small samples will lead to low accuracy and poor effectiveness of the training model, and the unbalanced samples will lead to deviations in the prediction results of the model, resulting in poor identification accuracy of a few types of samples. Therefore, a few samples are enhanced by introducing VAE to improve the effectiveness of the model. Secondly, 23 features which can reflect the dynamic characteristics of the waveform are extracted in time domain, the optimal expression feature group is selected from them by Gaussian kernel Fisher discriminant analysis (GKFDA) and maximal information coefficient (MIC). Through data analysis, various index features can be extracted from the changing forms of biological electric-shock waveforms. The addition of high-quality features will improve the diagnostic accuracy of the classifier to a certain extent, but the introduction of bad and redundant features will increase the running time of the algorithm and reduce the diagnostic accuracy of the classifier. Therefore, GKFDA and MIC are combined to perform feature scoring for each feature, and the optimal expression feature group is selected intuitively and independently based on the scoring results, which could improve the feature quality and reflect the regularity of feature selection. Finally, a forgetting-factor-based online sequential extreme learning machine (FOS-ELM) algorithm is investigated to identify the electric-shock behavior. There are abundant electric-shock scenes in the real environments. The escape behaviors of living objects during electric shock will have a great influence on the electric-shock waveform, which makes it difficult for the traditional off-line classifier to have adaptability. The online sequential extreme learning machine (OS-ELM) has an online learning mechanism that allows online updates for new samples without the historical data. The forgetting factor is introduced to form FOS-ELM, aiming to further solve the shortcoming of slow learning speed of OS-ELM, so that it can quickly adapt to changes of environmental samples with higher learning efficiency. The experimental data of conventional grounding fault and biological electric-shock fault in 12 scenes were collected for the verification of the proposed algorithm. The results show that the diagnosis accuracy of the proposed model can reach 98.75%, among which all 40 conventional grounding fault samples are correctly judged with an accuracy of 100%, while only 1 of 40 actual biological electric-shock fault samples is wrong with an accuracy of 97.5%. From the perspective of time, the average online learning time is 1.378 ms, and the average diagnosis time is only 1.33 ms. © 2024 China Machine Press. All rights reserved.
Keyword ：
Bioinformatics Bioinformatics Computer aided diagnosis Computer aided diagnosis Data mining Data mining E-learning E-learning Electric grounding Electric grounding Feature extraction Feature extraction Learning algorithms Learning algorithms Time domain analysis Time domain analysis
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| GB/T 7714 | Gao, Wei , Rao, Junmin , Quan, Shengxin et al. Biological Electric-Shock Fault Identification Method Based on Multi-Feature Optimization Selection under Unbalanced Small Sample [J]. | Transactions of China Electrotechnical Society , 2024 , 39 (7) : 2060-2071 . |
| MLA | Gao, Wei et al. "Biological Electric-Shock Fault Identification Method Based on Multi-Feature Optimization Selection under Unbalanced Small Sample" . | Transactions of China Electrotechnical Society 39 . 7 (2024) : 2060-2071 . |
| APA | Gao, Wei , Rao, Junmin , Quan, Shengxin , Guo, Moufa . Biological Electric-Shock Fault Identification Method Based on Multi-Feature Optimization Selection under Unbalanced Small Sample . | Transactions of China Electrotechnical Society , 2024 , 39 (7) , 2060-2071 . |
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针对现有配电网单相接地故障选段方法存在的通信负担重、阈值设置困难及故障特征量单一等问题,提出一种基于多频带多特征融合的配电网单相接地故障选段方法.首先,利用小波包变换分别对故障后首半个工频周期的零序电压导数和零序电流进行分解和重构,得到原始波形的不同频带分量.其次,根据不同频带分量间的极性关系分别引入伏安特性特征向量与零序功率累加和作为故障特征.最后,筛选特征频带并构造基于特征频带与故障特征融合的选段判据.仿真实验及现场数据验证结果表明,所提方法能够有效实现单相接地故障免阈值就地选段.
Keyword ：
免阈值 免阈值 单相接地故障 单相接地故障 多频带多特征融合 多频带多特征融合 小波包变换 小波包变换 故障选段 故障选段 谐振接地系统 谐振接地系统
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| GB/T 7714 | 肖文妍 , 郭谋发 , 林佳壕 et al. 基于多频带多特征融合的配电网单相接地故障选段方法 [J]. | 电气技术 , 2024 , 25 (7) : 7-14 . |
| MLA | 肖文妍 et al. "基于多频带多特征融合的配电网单相接地故障选段方法" . | 电气技术 25 . 7 (2024) : 7-14 . |
| APA | 肖文妍 , 郭谋发 , 林佳壕 , 林骏捷 . 基于多频带多特征融合的配电网单相接地故障选段方法 . | 电气技术 , 2024 , 25 (7) , 7-14 . |
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There is a problem in that the non-fault phase bridge arm of traditional three-phase direct-hanging flexible arc suppression device bears the line voltage. Thus a fault phase bridge arm transfer grounding arc suppression method is proposed. This method controls the fault phase bridge arm transfer grounding through the switch. The bus voltage is shared by the fault phase bridge arm and the non-fault phase bridge arm, and the three-phase bridge arm outputs the current to achieve arc suppression. The cascaded H-bridge DC-link needs an independent power supply, so a distributed voltage balance commutation modulation method is proposed. The modulation method selectively changes the H-bridge output state according to the influence of H-bridge output state and current direction on the charge and discharge of the DC-link capacitor, and then realizes the stability of DC-link voltage. The arc suppression device using this modulation method does not need to be equipped with an independent power supply on the DC side, and can maintain H-bridge DC-link voltage stability during the arc suppression process. Finally, the proposed method is verified by Matlab/Simulink simulation software, and the results prove the effectiveness of the proposed method. © 2024 Power System Protection and Control Press. All rights reserved.
Keyword ：
Electric grounding Electric grounding Electric power system protection Electric power system protection HVDC power transmission HVDC power transmission MATLAB MATLAB Solar power generation Solar power generation
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| GB/T 7714 | Guo, Moufa , Liu, Xinbin , Zhang, Binlong et al. DC-link voltage control method of a fault phase bridge arm transfer grounding arc suppression device [J]. | Power System Protection and Control , 2024 , 52 (19) : 1-14 . |
| MLA | Guo, Moufa et al. "DC-link voltage control method of a fault phase bridge arm transfer grounding arc suppression device" . | Power System Protection and Control 52 . 19 (2024) : 1-14 . |
| APA | Guo, Moufa , Liu, Xinbin , Zhang, Binlong , Zhao, Guojun , Zheng, Zeyin . DC-link voltage control method of a fault phase bridge arm transfer grounding arc suppression device . | Power System Protection and Control , 2024 , 52 (19) , 1-14 . |
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为了加强配电网自动化技术课程建设,提出基于虚实协同配合的实验教学方案.以"情景互动、探究创新"为实验教学理念,将课堂理论、虚拟仿真和物理仿真三者紧密结合.从需求导向出发,设计了四个渐进式实验,通过沉浸式教学,让学生了解配电网和开关设备的结构形态,掌握配电网运行、故障与保护算法的原理,培养学生的创新思维和创新设计能力.最后,建立主、客观评价相结合的综合评价体系,对学生参与实验的每个环节进行评价,通过收集反馈信息,持续改进评价体系.
Keyword ：
实验教学 实验教学 物理仿真 物理仿真 虚拟仿真 虚拟仿真 配电网 配电网
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| GB/T 7714 | 林宝全 , 高伟 , 郭谋发 et al. 虚实协同的"配电网自动化技术"实验项目建设 [J]. | 电气电子教学学报 , 2024 , 46 (2) : 216-220 . |
| MLA | 林宝全 et al. "虚实协同的"配电网自动化技术"实验项目建设" . | 电气电子教学学报 46 . 2 (2024) : 216-220 . |
| APA | 林宝全 , 高伟 , 郭谋发 , 谢楠 . 虚实协同的"配电网自动化技术"实验项目建设 . | 电气电子教学学报 , 2024 , 46 (2) , 216-220 . |
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